AN EMPIRICAL STUDY OF THE BEHAVIOR OF A FACE RECOGNITION SYSTEM BASED ON EIGENFACES AND K-NEAREST NEIGHBORS TECHNIQUES

Abstract

Developing a face recognition computational model is a hard task. Extracting facial features from facial images becomes a hard when the images have different dimensions, especially in the steps of extraction and classification. In this paper, we propose an empirical study of optimization on the rate accuracy results from a facial recognition based on Eigenfaces and K-Nearest Neighbors techniques. It was investigated the following topics: images with three different dimensions, number of features (Eigenfaces), k values from K-Nearest Neighbors technique and three distance measures. Addressing the problems of image dimensionality for facial recognition, understanding which parameters are more relevant from the addressed techniques in order to enhance the accuracies rate of facial recognition were the goals of this study. Following this, it was proved from the experiments that images with 12x9 sizes produce the best facial recognition accuracies rate, using the normalized Euclidean distance and a number of Eigenfaces equals to twenty.